支持向量机规则提取在大脑胶质瘤诊断中的应用  

Rule Induction Algorithm for Brain Glioma Using Support Vector Machine

作　　者：李国正[1] 杨杰[1] 王嘉驹[1] 耿道颖[2] 

机构地区：[1]上海交通大学图像处理与模式识别研究所 [2]复旦大学附属华山医院,上海200040

出　　处：《生物医学工程学杂志》2006年第2期410-412,共3页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(20175013)

摘　　要：利用一种新型的数据挖掘技术-支持向量机从大脑胶质瘤病例中获取胶质瘤良恶性程度的诊断知识。所获取的胶质瘤数据集有280个病例,其中多项信息包含了模糊值,还有一项有缺失值,致使人工神经网络算法在学习时易于产生过拟合,而支持向量机实现了统计学习理论中的结构风险最小化原理,克服了过拟合问题,并且其分类面是一个线性超平面,有定量关系表达式,所以计算所得到的结果无论从测试样本的平均准确率,还是所获取知识的可理解性等方面,都优于常用的神经网络和规则提取方法。A new proposed data mining technique, support vector machine （SVM）, is used to predict the degree of malignancy in brain glioma. Based on statistical learning theory, SVM realizes the principle of data dependent structure risk minimization, so it can depress the overfitting with better generalization performance, since the prediction in medical diagnosis often deals with a small sample. SVM based rule induction algorithm is implemented in comparison with other data mining techniques such as artificial neural networks, rule induction algorithm and fuzzy rule extraction algorithm based on fuzzy max-min neural networks （FRE-FMMNN） proposed recently. Computation results by 10 fold cross validation method show that SVM can get higher prediction accuracy than artificial neural networks and FRE-FMMNN, which implies SVM can get higher accuracy and more reliability. On the whole data sets, SVM gets one rule with the classification accuracy of 89.29%, while FRE- FMMNN gets two rules of 84. 64%, in which the rule got by SVM is of quantity relation and contains more information than the two rules by FRE-FMMNN. All the above show SVM is a potential algorithm for the medical diagnosis such as the prediction of the degree of malignancy in brain glioma.

关 键 词：支持向量机 规则提取 大脑胶质瘤 

分 类 号：R739.41[医药卫生—肿瘤] R730.4[医药卫生—临床医学]